Author
Park, Eunchun
Fed Cattle Price Relationships
Crop yield densities are often estimated at the county level. However, county-level yield data providers often omit county records due to low participation or other reasons. The data omission can undermine insurance premiums’ credibility and thereby lead to restrictions on the provision of area insurance products in specific locations. To address this problem, we propose a novel Bayesian spatial interpolation method to estimate crop yield densities for counties with missing data. Empirical results indicate that our approach is consistently superior to the benchmark approaches. Importantly, our approach offers noticeable estimation accuracy even at a significant level of data omission.
Many crop insurance studies have pointed out that considering spatial yield similarity can help provide more precise premium rating. We use Bayesian Kriging for spatial smoothing to consider such similarities when estimating crop yield densities. This article’s innovation is that the spatial smoothing is based on climate space, which is composed of climatological measures. We compare the climate-space smoothing with a general physical space (longitude–latitude space) smoothing. The test results are favorable to the proposed climate-smoothing method. Climate smoothing performs particularly well in states that have many missing counties and varied climate due to varying topography.